In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. You will also be able to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.

Taught By

Darren Cook

Transcript

A short, simple video on loading and saving the models that you're making. So, it's really easy to save a model. Just call save model. And once you've save that model, it's really easy to load it back in again in a later session. Couple of things to watch out for. First, remember the client server. The path you specify is always relative to the server. So, if you're using a remote cluster, it's the cluster's file system you're loading and saving from. The second thing is model ID. The model ID is used as the file name when saving. And when you load back in, it will keep the same model ID it had when you saved it. So just watch out for clashes. So you can't create model called test, save it, create another model called test, save it, and then try and load both of them back into the same session. Basically, load and save works very easily exactly as you'd expect it to.

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